Abstract
Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer, and Operator (CTO), is proposed. CTO employs a combination of Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and an explicit boundary detection operator to achieve high recognition accuracy while maintaining an optimal balance between accuracy and efficiency. The proposed CTO follows the standard encoder-decoder segmentation paradigm, where the encoder network incorporates a popular CNN backbone for capturing local semantic information, and a lightweight ViT assistant for integrating long-range dependencies. To enhance the learning capacity on boundary, a boundary-guided decoder network is proposed that uses a boundary mask obtained from a dedicated boundary detection operator as explicit supervision to guide the decoding learning process. The performance of the proposed method is evaluated on six challenging medical image segmentation datasets, demonstrating that CTO achieves state-of-the-art accuracy with a competitive model complexity.
Abstract (translated)
医学图像分割是医学图像分析社区的一项基本任务。在本文中，我们提出了一种新的网络架构，称为卷积、Transformer和操作(CTO)。CTO采用卷积神经网络(CNNs)、视觉Transformer(ViT)和明确的边界检测操作来实现高识别精度，同时保持精度和效率的最优平衡。我们提出了一种标准的编码-解码分割范式，其中编码网络采用流行的CNN基线以捕捉 local 语义信息，并使用轻量级的ViT助手以整合长期依赖关系。为了增强边界的学习能力，我们提出了一种边界引导的解码网络，使用专门用于边界检测的操作的边界掩码作为明确的监督来指导解码学习过程。我们针对六个具有挑战性的医学图像分割数据集进行了性能评估，证明了CTO实现了先进的精度，同时具有竞争力模型复杂性。
URL
https://arxiv.org/abs/2305.00678
